Approaches for scaling DBSCAN algorithm to large spatial databases

The huge amount of information stored in datablases owned by coporations(e.g.retail,financial,telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining.Clustering.in data mining,is a useful technique for discovering intersting data distributions and patterns in the underlying data,and has many application fields,such as statistical data analysis,pattern recognition,image processsing,and other business application,s Although researchers have been working on clustering algorithms for decades,and a lot of algorithms for clustering have been developed,there is still no efficient algorithm for clustering very large databases and high dimensional data,As an outstanding representative of clustering algorithms,DBSCAN algorithm shows good performance in spatial data clustering.However,for large spatial databases,DBSCAN requires large volume of memory supprot and could incur substatial I/O costs because it operates directly on the entrie database,In this paper,several approaches are proposed to scale DBSCAN algorithm to large spatial databases.To begin with,a fast DBSCAN algorithm is developed.which considerably speeeds up the original DBSCAN algorithm,Then a sampling based DBSCAN algorithm,a partitioning-based DBSCAN algorithm,and a parallel DBSCAN algorithm are introduced consecutively.Following that ,based on the above-proposed algorithms,a synthetic algorithm is also given,Finally,some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.     

Generic regular decompositions for generic zero-dimensional systems

Two new concepts,generic regular decomposition and regular-decomposition-unstable(RDU)variety for generic zero-dimensional systems,are introduced in this paper and an algorithm is proposed for computing a generic regular decomposition and the associated RDU variety of a given generic zero-dimensional system simultaneously.The solutions of the given system can be expressed by finitely many zero-dimensional regular chains if the parameter value is not on the RDU variety.The so called weakly relatively simplicial decomposition plays a crucial role in the algorithm,which is based on the theories of subresultants.Furthermore,the algorithm can be naturally adopted to compute a non-redundant Wu’s decomposition and the decomposition is stable at any parameter value that is not on the RDU variety.The algorithm has been implemented with Maple 16 and experimented with a number of benchmarks from the literature.Empirical results are also presented to show the good performance of the algorithm.     

Performance Evaluation of Lateration,KNN and Artificial Neural Networks Techniques Applied to Real Indoor and Outdoor Location in WSN

WSN （wireless sensors networks） is a promising research area which has been in center of attention in many applications on telecommunications. Despite of many existing applications, a bunch of algorithms have been created or applied to solve different issues surrounding WSN. An attractive subtopic on research area is Localization Algorithms. Due to the countless applications, it is almost impossible to list all the algorithms applicable to solve sensor＇s location problem in WSN over distinguished parameters associated to diverse environments. This paper evaluates a geometrical algorithm, an instance based algorithm and a function approximator algorithm, having the RSS/ （received signal strength indicator） as metric to estimate planar coordinates in an indoor and outdoor environment using a WSN based on IRIS mote. The analysis of the WSN is constructed over statistical data obtained from empirical experiments and the observed characteristics of the algorithms. We also estimate the performance for different parameters configurations applied to the algorithms for both indoor and outdoor environment. Also, some comments about the tradeoff between the accuracy and the performance of the algorithms are made objecting to. Also, some objections about the tradeoff between the accuracy and the performance of the algorithms are made when relevant.     

A self-adaptive linear evolutionary algorithm for solving constrained optimization problems

In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions.     

Secure Multi-party θ-join Algorithms Toward Data Federation

Recently, many countries and regions have enacted data security policies, such as the General Data Protection Regulation proposed by the EU. The release of related laws and regulations has aggravated the problem of data silos, which makes it difficult to share data among various data owners. Data federation is a possible solution to this problem. Data federation refers to the calculation of query tasks jointly performed by multiple data owners without original data leaks using privacy computing technologies such as secure multi-party computing. This concept has become a research trend in recent years, and a series of representative systems have been proposed, such as SMCQL and Conclave. However, for the core join queries in the relational database system, the existing data federation system still has the following problems. First of all, the join query type is single, which is difficult to meet the query requirements under complex join conditions. Secondly, the algorithm performance has huge improvement space because the existing systems often call the security tool library directly, which means the runtime and communication overhead is high. Therefore, this paper proposes a join algorithm under data federation to address the above issues. The main contributions of this paper are as follows: firstly, multi-party-oriented federation security operators are designed and implemented, which can support many operations. Secondly, a federated θ-join algorithm and an optimization strategy are proposed to significantly reduce the security computation cost. Finally, the performance of the algorithm proposed in this paper is verified by the benchmark dataset TPC-H. The experimental results show that the proposed algorithm can reduce the runtime and communication overhead by 61.33% and 95.26%, respectively, compared with the existing data federation systems SMCQL and Conclave.     

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters(λ and ξ), but the relation of them is not linear. So it is hard to find a best combination of λ and ξ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.     

A New Subdivision Algorithm for the Bernstein Polynomial Approach to Global Optimization

In this paper,an improved algorithm is proposed for unconstrained global optimization to tackle non-convex nonlinear multivariate polynomial programming problems.The proposed algorithm is based on the Bernstein polynomial approach.Novel features of the proposed algorithm are that it uses a new rule for the selection of the subdivision point,modified rules for the selection of the subdivision direction,and a new acceleration device to avoid some unnecessary subdivisions.The performance of the proposed algorithm is numerically tested on a collection of 16 test problems.The results of the tests show the proposed algorithm to be superior to the existing Bernstein algorithm in terms of the chosen performance metrics.     

Performance Analysis of Round Robin Scheduling for Session-based Applications on Clusters

Nowadays session-based applications are one of the typical applications in the Internet,and people build such applications on clusters on concern of scalability. Scheduling in such a cluster is a key technology since system performance depends on it. In this paper,we investigate the Round-Robin algorithm in the context of Session-based applications. An analyzing model for such sys-tems is proposed. Through both theoretical analysis and simulation,we find the main factor for system performance. And the result also shows that this algorithm shows up with significantly different performance under various conditions.     

Towards Effective Integrated Access Control Lists in Internet Networks

ACLs （access control lists） represent a traditional way in filtering packets in routers. In modern complex enterprise networks that provide a vast array of services, there is an ever increasing need for verifying the integrity of ACLs to detect any potential security holes and improve the network performance. This paper concerns the integrity of routers＇ ACLs in large enterprise networks. We first investigate the integrity of the ACLs of two touters by describing a bottom-up approach for detecting redundancies in ACLs of two routers. We then extend our study to multiple touters and provide a heuristic algorithm for detecting redundant ACLs in multiple touters. We validate the practicality of ouralgorithm through real-life and synthetic router ACL groups of large networks. Performance results show that our heuristic algorithm do not only improve the performance by reducing the number of comparisons overhead, but also helps in discovering potential security holes that can not be discovered by considering the ACLs of each router individually.     

Decrement delay with widest K-shortest paths Q-routing algorithm

During the last few years the Internet has grown tremendously and has penetrated all aspects of everyday life. Starting off as a purely academic research network, the Internet is now extensively used for education, for entertainment, and as a very promising and dynamic marketplace, and is envisioned as evolving into a vehicle of true collaboration and a multi-purpose working environment. Although the lnternet is based on a best-effort service model, the simplicity of its packet-switched design and the flexibility of its underlying packet forwarding regime （IP） accommodate millions of users while offering acceptable performance. At the same time, exciting new applications and networked services have emerged, putting greater demands on the network. In order to offer a better-than-best-effort Internet, new service models that offer applications performance guarantees have been proposed. While several of these proposals are in place, and many QOS-enabled networks are operating, there is still a lack of comprehension about the precise requirements new applications have in order to function with high or acceptable levels of quality. Furthermore, what is required is an understanding of how network-level QOS reflects on actual application utility and usability. In this work a proposal for routing which improves the delay factor and is based on the reinforcement learning is concerned. We use Q-learning as the reinforcement learning technique and introduce K-shortest idea into the learning process. The proposed algorithm is applied to two different topologies. The OPNET is used to evaluate the performance of the proposed algorithm. The algorithm evaluation is done for two traffic conditions, namely low load and high load.     

基于三方传送的USN的设计和实现

USN（统一存储网）为用户同时提供File I／O和Block I／O服务,实现了NAS和SAN的统一。采用三方传送协议实现USN可以达到以下目的：元数据服务器集中管理存储设备实现了存储空间的动态共享,提高了存储空间的利用率;合理地分配信用保证了数据共享的一致性;利用密钥分配方案生成信用在保证存储安全的同时而不增加元数据服务器的过重的负担;元数据通道和数据通道的分离,提高了USN的I／O性能。实验证明该方法是可行的。     

一种高性能的网络文件访问协议的研究与实现

随着网络上的数据呈爆炸性的增长，融合NAS和SAN技术的USN(统一存储网)正成为新的研究热点。针对传统文件共享协议的缺点，实现了一个新的基于USN的文件共享协议(File Access Protocol，FAP)，详细介绍了这种FAP协议的设计方案，并进行了相应的试验测试和性能分析。     

基于iSCSI OSD存储系统的设计与分析

iSCSI是一种高效的、高可扩展的存储协议,而对象存储系统是一种新兴的跨平台、安全性的网络存储架构。设计实现的基于iSCSI OSD存储系统,充分发挥了两者的优点。实验测试证明该系统具有较高的存储性能。     

统一存储网中的虚拟存储技术研究

随着网络存储技术的发展,存储局域网和附网存储的融合是大势所趋。该文详细介绍了构建一个统一的存储网络的关键技术——存储虚拟化,并提出统一存储网的实验方案,针对其中与存储虚拟化有关的具体问题进行了探讨和研究。     

SDN环境下基于DBN的DDoS攻击检测

软件定义网络(SDN)作为新型网络架构模式,其安全威胁主要来自DDoS攻击,建立高效的DDoS攻击检测系统是网络安全管理的重要内容.在SDN环境下,针对DDoS的入侵检测算法具有支持协议少、实用性差等缺陷,为此,提出一种基于深度信念网络(DBN)的DDoS攻击检测算法.分析SDN环境下DDoS攻击的机制,通过Mininet模拟SDN的网络拓扑结构,并使用Wireshark完成DDoS流量数据包的收集和检测.实验结果表明,与XGBoost、随机森林、支持向量机算法相比,该算法具有攻击检测准确性高、误报率低、检测速率快和易于扩展等优势,综合性能较好.     

一种IP网络存储协议iBDTP的设计与实现

目前可用于IP存储广域网的IP网络存储协议包括iSCSI、HyperSCSI、ENBD等,它们在支持多类型设备接口、支持TCP/IP协议以及设备自动发现机制上存在诸多不足。为了克服这些不足,文章设计了一种iBDTP协议,它支持多种类型的设备接口,并在IP广域网范围内具有设备的自动发现机制。此外,在iBDTP基础上,该文利用基本的IP广域网结构,构建了IP存储广域网,并对基于iBDTP协议的IP存储广域网的性能进行了分析。     

存储区域网络技术在数据处理中心的应用

随着高性能计算技术的发展,实现对大规模数据的高效存取已成为行业中提高系统整体性能的关键。传统的NFS存储架构,由于其I/O存取带宽低,容易造成TCP/IP局域网络的瓶颈,因此制约了系统整体性能的发挥。为了有效地缓解TCP/IP网络瓶颈,提高海量数据的访问效率,实现网络中各服务器节点对地震数据的共享,我中心自2004年起先后两次进行了存储区域网络的构建,并安装了ADIC公司的SNFS存储共享软件。本文重点介绍了存储区域网的构建、整合方法,并对存储共享软件SNFS的安装与配置做了简单介绍,对其它数据处理中心的存储区域网络构建和应用具有一定的借鉴作用。     

改进的ECC算法在网络信息安全中的研究

目前网络信息安全遭受许多网络威胁,现有的加密算法已经无法满足网络信息安全的需求。提出了一种基于网络信息安全的改进ECC算法,该算法基于原有的ECC算法,对其进行点积运算的优化和平方剩余判定的优化,并对私钥更新变换进行了优化,以提高原有ECC算法的运算效率和安全性能。实验表明,基于网络信息安全的改进ECC算法在安全性能上比常用的RSA算法以及原有的ECC算法都有显著提高,该方案切实有效。     

安全高效的SSL VPN构建方法研究

随着互联网技术和通信需求的迅猛发展,虚拟专用网（VPN）已成为构建专用网的主要方法。与其它虚拟专用网相比,安全套接层虚拟专用网（SSL VPN）由于具有安装简单、能够进行细粒度控制等突出优点,在构建远程访问VPN（Access VPN）时得到了广泛应用。但是,SSL VPN在构建过程中也面临着安全问题和效率问题,这些问题严重制约着SSL VPN的发展。该文在对这些问题进行分析和研究的基础上,提出了全新的SSLVPN安全认证方案和网络加速方案,并推导出配置加速器数目的计算方法。     

分散服务集中管理的NAS集群方案研究与实现

通过分析传统集群方案的基本原理、体系结构，深入剖析了传统集群方案的优缺点，提出了一种高性能的NAS集群方案。它对用户采用分散服务，对全局源数据采用集中管理，发挥了分散和集中的各自优势，并详细讨论了它的实现原理。